Classifications

• • C—CHEMISTRY; METALLURGY
  • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
  • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
  • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
  • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
• • C—CHEMISTRY; METALLURGY
  • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
  • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
  • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
  • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
  • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
  • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
  • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
• • C—CHEMISTRY; METALLURGY
  • C40—COMBINATORIAL TECHNOLOGY
  • C40B—COMBINATORIAL CHEMISTRY; LIBRARIES, e.g. CHEMICAL LIBRARIES
  • C40B40/00—Libraries per se, e.g. arrays, mixtures
  • C40B40/04—Libraries containing only organic compounds
  • C40B40/06—Libraries containing nucleotides or polynucleotides, or derivatives thereof
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
  • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
  • G16B20/20—Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
• • C—CHEMISTRY; METALLURGY
  • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
  • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
  • C12Q2600/00—Oligonucleotides characterized by their use
  • C12Q2600/156—Polymorphic or mutational markers

Definitions

• the present disclosure relates to methods and systems for assessing the risk of a human subject for developing colorectal cancer. These methods may be combined with the subjects clinical risk to improve risk analysis. Such methods may be used to assist decision making about appropriate colorectal cancer screening regimens.
• Colorectal cancer screening programs advocate administering tests to individuals across apparently healthy populations to identify individuals who have either pre-malignant or early stages of colorectal cancer so that they may benefit from prevention or early treatment. Screening tests can include fecal occult blood testing and colonoscopy. In the average risk population, screening based on fecal occult blood testing reduces colorectal mortality by 15% to 25% (Hewitson et al., 2007). Endoscopic screening can reduce mortality by 30% to 40% (Brenner et al., 2014).
• Genetic risk assessments may increase screening program efficiency.
• genetic susceptibility to inherited colorectal cancer is complex and involves multiple variants and genes.
• the present inventors have identified SNP's within the genome that are useful for assessing the risk of a subject developing colorectal cancer.
• the present disclosure relates to a method for assessing the risk of a human subject for developing colorectal cancer comprising: performing a genetic risk assessment of the subject, wherein the genetic risk assessment involves detecting, in a biological sample derived from the subject, the presence of at least 28 single nucleotide polymorphisms selected from Table 1, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.
• the genetic risk assessment at least comprises detecting the presence of single nucleotide polymorphisms rs3987, rs35509282 and rs744166, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.
• the genetic risk assessment comprises detecting more than 28 single nucleotide polymorphisms selected from Table 1, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof. For example, at least 29, at least 30, at least 31, at least 32, at least 33, at least 34, at least 35, at least 36, at least 37, at least 38, at least 39, at least 40, at least 41, at least 42, at least 43, at least 44 single nucleotide polymorphisms may be detected. In another embodiment, at least 45 single nucleotide polymorphisms are detected.
• the genetic risk assessment comprises detecting the presence of single nucleotide polymorphism rs5934683, or a single nucleotide polymorphism in linkage disequilibrium thereof.
• the genetic risk assessment is combined with a clinical risk assessment to obtain the risk of a human subject for developing colorectal cancer.
• the clinical risk assessment involves obtaining information from the subject on one or more of the following: medical history of colorectal cancer, age, family history of colorectal cancer, results of previous colonoscopy or sigmoidoscopy screening and race/ethnicity.
• the clinical risk assessment involves obtaining information from the subject on age and/or first degree relative's history of colorectal cancer.
• family history of colorectal cancer includes multigenerational family history.
• the combined clinical risk assessment and genetic risk assessment defines the subjects overall risk for developing colon cancer.
• the methods of the invention can be used to assess overall risk.
• the methods of the present disclosure determine the absolute risk of a human female subject for developing colon cancer.
• the methods of the present disclosure determine the relative risk of a human female subject for developing colon cancer.
• the methods of the present disclosure may be applicable to subjects with symptoms of colorectal cancer.
• subjects that have had a positive fecal occult blood test can be assessed using the methods of the present disclosure.
• Fecal occult blood testing is generally recommended to subjects around 50 years of age.
• the present inventors have found that certain individuals are at increased risk of colorectal cancer well before they reach 50 years of age, in particular if a first degree relative has been diagnosed with colorectal cancer. These findings suggest that some individuals should be assessed earlier to determine whether they are at risk of colorectal cancer.
• subjects assessed using the methods of the present disclosure are at least 40 years of age.
• the subject assessed is by at least 30 years of age if a first degree relative has been diagnosed with colorectal cancer.
• the subject may be male or female. In another embodiment, the subject is male.
• Subjects determined to be at risk of developing colorectal cancer using the present invention may then be enrolled in a screening program or subjected to more frequent screening.
• performance of the disclosed methods is characterized by an area under the curve (AUC) of at least about 0.63.
• a single nucleotide polymorphism in linkage disequilibrium has linkage disequilibrium above 0.9. In another embodiment, a single nucleotide polymorphism in linkage disequilibrium has linkage disequilibrium of 1.
• the methods of the present disclosure are used to determine the need for routine diagnostic testing of a human subject for colorectal cancer. For example, when factoring in that each of the single nucleotide polymorphisms may be present up to twice in the somatic diploid genome of the subject, a subject having at least 41, at least 42, at least 44, at least 46, at least 50, at least 55, at least 60, at least 65, or at least 70, of the single nucleotide polymorphisms should be enrolled in a fecal occult screening, colonoscopic or sigmoidoscopic screening program.
• the assessment places the subject in the top 20% of subjects in a population at risk of developing colorectal cancer the subject is enrolled in a fecal occult screening, colonoscopic or sigmoidoscopic screening program. In another embodiment, if the assessment places the subject in the top 10% of subjects in a population at risk of developing colorectal cancer the subject is enrolled in a fecal occult screening, colonoscopic or sigmoidoscopic screening program.
• the present invention provides a method of screening for colorectal cancer in a human subject, the method comprising assessing the risk of the subject for developing colorectal cancer using the method of the invention, and routinely screening for colorectal cancer in the subject if they are assessed as having a risk for developing colorectal cancer.
• the methods of the present disclosure are used as an anti- colorectal cancer therapy for use in preventing colorectal cancer in a human subject at risk thereof.
• the present disclosure relates to a kit comprising at least 28 sets of primers for amplifying 28 or more nucleic acids, wherein the 28 or more nucleic acids comprise a single nucleotide polymorphism selected from Table 1, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.
• the present disclosure relates to a genetic array comprising at least 28 sets of probes for hybridising to 28 or more nucleic acids, wherein the 28 or more nucleic acids comprise a single nucleotide polymorphism selected from Table 1, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.
• the present disclosure relates to a computer implemented method for assessing the risk of a human subject for developing colorectal cancer, the method operable in a computing system comprising a processor and a memory, the method comprising:
• genetic risk data for the subject, wherein the genetic risk data was obtained by detecting, in a biological sample derived from the subject, the presence of at least 28 single nucleotide polymorphisms from Table 1, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof;
• the computer implemented method further comprises receiving clinical risk data for the subject;
• the risk data for the subject is received from a user interface coupled to the computing system.
• the risk data for the subject is received from a remote device across a wireless communications network.
• the user interface or remote device is a SNP array platform.
• outputting comprises outputting information to a user interface coupled to the computing system.
• outputting comprises transmitting information to a remote device across a wireless communications network.
• composition of matter, group of steps or group of compositions of matter shall be taken to encompass one and a plurality (i.e. one or more) of those steps, compositions of matter, groups of steps or group of compositions of matter.
• Figure 1 The simulated distribution of risk alleles for 1,000,000 people with a history of colorectal cancer (red) and 1,000,000 people without a history of colorectal cancer (blue); and the cumulative risk of colorectal cancer to age 70 years for the number of risk alleles for an Australian (square) and USA (circle) population.
• FIG. Australian risks of colorectal cancer (males and females combined) by age category, family history of colorectal cancer (first-degree relative) and by number of risk alleles.
• Panel A cumulative risks to age 70 with highest and lowest quintiles for number of risk alleles.
• Panel B cumulative risks to age 70 with highest and lowest deciles for number of risk alleles.
• Panel C 5-year risks with highest and lowest quintiles for number of risk alleles.
• Panel D 5-year risks with highest and lowest deciles for number of risk alleles.
• Figure 3 USA risks of colorectal cancer (males and females combined) by age category, family history of colorectal cancer (first-degree relative) and by number of risk alleles.
• Panel A cumulative risks to age 70 with highest and lowest quintiles for number of risk alleles.
• Panel B cumulative risks to age 70 with highest and lowest deciles for number of risk alleles.
• Panel C 5-year risks with highest and lowest quintiles for number of risk alleles.
• Panel D 5-year risks with highest and lowest deciles for number of risk alleles.
• FIG 4. Australian risks of colorectal cancer (males) by age category, family history of colorectal cancer (first-degree relative) and by number of risk alleles.
• Panel A cumulative risks to age 70 with highest and lowest quintiles for number of risk alleles.
• Panel B cumulative risks to age 70 with highest and lowest deciles for number of risk alleles.
• Panel C 5-year risks with highest and lowest quintiles for number of risk alleles.
• Panel D 5-year risks with highest and lowest deciles for number of risk alleles.
• Figure 5. Australian risks of colorectal cancer (females) by age category, family history of colorectal cancer (first-degree relative) and by number of risk alleles.
• Panel A cumulative risks to age 70 with highest and lowest quintiles for number of risk alleles.
• Panel B cumulative risks to age 70 with highest and lowest deciles for number of risk alleles.
• Panel C 5-year risks with highest and lowest quintiles for number of risk alleles.
• Panel D 5-year risks with highest and lowest deciles for number of risk alleles.
• Figure 6 USA risks of colorectal cancer (males) by age category, family history of colorectal cancer (first-degree relative) and by number of risk alleles.
• Panel A cumulative risks to age 70 with highest and lowest quintiles for number of risk alleles.
• Panel B cumulative risks to age 70 with highest and lowest deciles for number of risk alleles.
• Panel C 5-year risks with highest and lowest quintiles for number of risk alleles.
• Panel D 5-year risks with highest and lowest deciles for number of risk alleles.
• Figure 7. USA risks of colorectal cancer (females) by age category, family history of colorectal cancer (first-degree relative) and by number of risk alleles.
• Panel A cumulative risks to age 70 with highest and lowest quintiles for number of risk alleles.
• Panel B cumulative risks to age 70 with highest and lowest deciles for number of risk alleles.
• Panel C 5-year risks with highest and lowest quintiles for number of risk alleles.
• Panel D 5-year risks with highest and lowest deciles for number of risk alleles.
• colonal cancer encompasses any type of cancer that can develop in the colon or rectum of a subject.
• colon cancer colon cancer
• the colorectal cancer may be characterised as T stage 1 - 4.
• the colorectal cancer may be characterised as Dukes stage A - D
• colonal cancer also encompasses a phenotype that displays a predisposition towards developing colorectal cancer in an individual.
• a phenotype that displays a predisposition for colorectal cancer can, for example, show a higher likelihood that the cancer will develop in an individual with the phenotype than in members of a relevant general population under a given set of environmental conditions (diet, physical activity regime, geographic location, etc.).
• the colorectal cancer may be classified clinically as pre-malignant (e.g. hyperplasia, adenoma).
• a "polymorphism” is a locus that is variable; that is, within a population, the nucleotide sequence at a polymorphism has more than one version or allele.
• One example of a polymorphism is a "single nucleotide polymorphism", which is a polymorphism at a single nucleotide position in a genome (the nucleotide at the specified position varies between individuals or populations).
• SNP single nucleotide polymorphism
• SNPs is the plural of SNP.
• DNA such reference may include derivatives of the DNA such as amplicons, RNA transcripts thereof, etc.
• allele refers to one of two or more different nucleotide sequences that occur or are encoded at a specific locus, or two or more different polypeptide sequences encoded by such a locus. For example, a first allele can occur on one chromosome, while a second allele occurs on a second homologous chromosome, e.g., as occurs for different chromosomes of a heterozygous individual, or between different homozygous or heterozygous individuals in a population.
• An allele "positively” correlates with a trait when it is linked to it and when presence of the allele is an indicator that the trait or trait form will occur in an individual comprising the allele.
• An allele “negatively” correlates with a trait when it is linked to it and when presence of the allele is an indicator that a trait or trait form will not occur in an individual comprising the allele.
• the term "risk allele” is used in the context of the present disclosure to refer to an allele indicating a genetic propensity to susceptibility to colorectal cancer.
• a subject can be homozygous, heterozygous or null for a particular risk allele.
• a marker polymorphism or allele is "correlated” or "associated” with a specified phenotype (colorectal cancer susceptibility, etc.) when it can be statistically linked (positively or negatively) to the phenotype.
• Methods for determining whether a polymorphism or allele is statistically linked are known to those in the art. That is, the specified polymorphism(s) occurs more commonly in a case population (e.g., colorectal cancer patients) than in a control population (e.g., individuals that do not have colorectal cancer). This correlation is often inferred as being causal in nature, but it need not be - simple genetic linkage to (association with) a locus for a trait that underlies the phenotype is sufficient for correlation/association to occur.
• LD linkage disequilibrium
• D' Lewontin's parameter of association
• r Pearson correlation coefficient
• Linkage disequilibrium is calculated following the application of the expectation maximization algorithm (EM) for the estimation of haplotype frequencies (Slatkin and Excoffier, 1996).
• LD values according to the present disclosure for neighbouring genotypes/loci are selected above 0.5, more preferably, above 0.6, still more preferably, above 0.7, preferably, above 0.8, more preferably above 0.9, ideally about 1.0.
• Many of the SNPs in linkage disequilibrium with the SNPs of the present disclosure that are described herein have LD values of 0.9 or 1.
• LOD stands for "logarithm of the odds", a statistical estimate of whether two genes, or a gene and a disease gene, are likely to be located near each other on a chromosome and are therefore likely to be inherited.
• a LOD score of between about 2 - 3 or higher is generally understood to mean that two genes are located close to each other on the chromosome.
• LOD values according to the present disclosure for neighbouring genotypes/loci are selected at least above 2, at least above 3, at least above 4, at least above 5, at least above 6, at least above 7, at least above 8, at least above 9, at least above 10, at least above 20 at least above 30, at least above 40, at least above 50.
• SNPs in linkage disequilibrium with the SNPs of the present disclosure can have a specified genetic recombination distance of less than or equal to about 20 centimorgan (cM) or less. For example, 15 cM or less, 10 cM or less, 9 cM or less, 8 cM or less, 7 cM or less, 6 cM or less, 5 cM or less, 4 cM or less, 3 cM or less, 2 cM or less, 1 cM or less, 0.75 cM or less, 0.5 cM or less, 0.25 cM or less, or 0.1 cM or less.
• centimorgan centimorgan
• two linked loci within a single chromosome segment can undergo recombination during meiosis with each other at a frequency of less than or equal to about 20%, about 19%, about 18%, about 17%, about 16%, about 15%, about 14%, about 13%, about 12%, about 11%, about 10%, about 9%, about 8%, about 7%, about 6%, about 5%, about 4%, about 3%, about 2%, about 1%, about 0.75%, about 0.5%, about 0.25%, or about 0.1% or less.
• SNPs in linkage disequilibrium with the SNPs of the present disclosure are within at least 100 kb (which correlates in humans to about 0.1 cM, depending on local recombination rate), at least 50 kb, at least 20 kb or less of each other.
• One exemplary approach for the identification of surrogate markers for a particular SNP involves a simple strategy that presumes that SNPs surrounding the target SNP are in linkage disequilibrium and can therefore provide information about disease susceptibility.
• Potentially surrogate markers can therefore be identified from publicly available databases, such as HAPMAP, by searching for SNPs fulfilling certain criteria which have been found in the scientific community to be suitable for the selection of surrogate marker candidates.
• Allele frequency refers to the frequency (proportion or percentage) at which an allele is present at a locus within an individual, within a line or within a population of lines. For example, for an allele “A,” diploid individuals of genotype “AA,””Aa,” or “aa” have allele frequencies of 1.0, 0.5, or 0.0, respectively. One can estimate the allele frequency within a line or population (e.g., cases or controls) by averaging the allele frequencies of a sample of individuals from that line or population. Similarly, one can calculate the allele frequency within a population of lines by averaging the allele frequencies of lines that make up the population.
• allele frequency is used to define the minor allele frequency (MAF).
• MAF refers to the frequency at which the least common allele occurs in a given population.
• An individual is “homozygous” if the individual has only one type of allele at a given locus (e.g., a diploid individual has a copy of the same allele at a locus for each of two homologous chromosomes).
• An individual is “heterozygous” if more than one allele type is present at a given locus (e.g., a diploid individual with one copy each of two different alleles).
• the term “homogeneity” indicates that members of a group have the same genotype at one or more specific loci. In contrast, the term “heterogeneity” is used to indicate that individuals within the group differ in genotype at one or more specific loci.
• locus is a chromosomal position or region.
• a polymorphic locus is a position or region where a polymorphic nucleic acid, trait determinant, gene or marker is located.
• a "gene locus” is a specific chromosome location (region) in the genome of a species where a specific gene can be found.
• a “marker,” “molecular marker” or “marker nucleic acid” refers to a nucleotide sequence or encoded product thereof (e.g., a protein) used as a point of reference when identifying a locus or a linked locus.
• a marker can be derived from genomic nucleotide sequence or from expressed nucleotide sequences (e.g., from an RNA, nRNA, mRNA, a cDNA, etc.), or from an encoded polypeptide.
• the term also refers to nucleic acid sequences complementary to or flanking the marker sequences, such as nucleic acids used as probes or primer pairs capable of amplifying the marker sequence.
• a “marker probe” is a nucleic acid sequence or molecule that can be used to identify the presence of a marker locus, e.g., a nucleic acid probe that is complementary to a marker locus sequence. Nucleic acids are "complementary” when they specifically hybridize in solution, e.g., according to Watson-Crick base pairing rules.
• a “marker locus” is a locus that can be used to track the presence of a second linked locus, e.g., a linked or correlated locus that encodes or contributes to the population variation of a phenotypic trait.
• a marker locus can be used to monitor segregation of alleles at a locus, such as a quantitative trait locus (QTL), that are genetically or physically linked to the marker locus.
• QTL quantitative trait locus
• a "marker allele,” alternatively an “allele of a marker locus” is one of a plurality of polymorphic nucleotide sequences found at a marker locus in a population that is polymorphic for the marker locus.
• the present disclosure provides marker loci correlating with a phenotype of interest, e.g., colorectal cancer.
• a phenotype of interest e.g., colorectal cancer.
• Each of the identified markers is expected to be in close physical and genetic proximity (resulting in physical and/or genetic linkage) to a genetic element, e.g., a QTL that contributes to the relevant phenotype.
• Markers corresponding to genetic polymorphisms between members of a population can be detected by methods well-established in the art.
• PCR-based sequence specific amplification methods include, e.g., PCR-based sequence specific amplification methods, detection of restriction fragment length polymorphisms (RFLP), detection of isozyme markers, detection of allele specific hybridization (ASH), detection of single nucleotide extension, detection of amplified variable sequences of the genome, detection of self-sustained sequence replication, detection of simple sequence repeats (SSRs), detection of single nucleotide polymorphisms (SNPs), or detection of amplified fragment length polymorphisms (AFLPs).
• RFLP restriction fragment length polymorphisms
• ASH allele specific hybridization
• SSRs simple sequence repeats
• SNPs single nucleotide polymorphisms
• AFLPs amplified fragment length polymorphisms
• amplifying in the context of nucleic acid amplification is any process whereby additional copies of a selected nucleic acid (or a transcribed form thereof) are produced.
• Typical amplification methods include various polymerase based replication methods, including the polymerase chain reaction (PCR), ligase mediated methods such as the ligase chain reaction (LCR) and RNA polymerase based amplification (e.g., by transcription) methods.
• PCR polymerase chain reaction
• LCR ligase chain reaction
• RNA polymerase based amplification e.g., by transcription
• An “amplicon” is an amplified nucleic acid, e.g., a nucleic acid that is produced by amplifying a template nucleic acid by any available amplification method (e.g., PCR, LCR, transcription, or the like).
• amplification method e.g., PCR, LCR, transcription, or the like.
• a specified nucleic acid is "derived from" a given nucleic acid when it is constructed using the given nucleic acid's sequence, or when the specified nucleic acid is constructed using the given nucleic acid.
• a “gene” is one or more sequence(s) of nucleotides in a genome that together encode one or more expressed molecules, e.g., an RNA, or polypeptide.
• the gene can include coding sequences that are transcribed into RNA which may then be translated into a polypeptide sequence, and can include associated structural or regulatory sequences that aid in replication or expression of the gene.
• Genotype is the genetic constitution of an individual (or group of individuals) at one or more genetic loci. Genotype is defined by the allele(s) of one or more known loci of the individual, typically, the compilation of alleles inherited from its parents.
• haplotype is the genotype of an individual at a plurality of genetic loci on a single DNA strand.
• the genetic loci described by a haplotype are physically and genetically linked, i.e., on the same chromosome strand.
• a "set" of markers, probes or primers refers to a collection or group of markers probes, primers, or the data derived therefrom, used for a common purpose (e.g., assessing an individuals risk of developing colorectal cancer). Frequently, data corresponding to the markers, probes or primers, or derived from their use, is stored in an electronic medium. While each of the members of a set possess utility with respect to the specified purpose, individual markers selected from the set as well as subsets including some, but not all of the markers, are also effective in achieving the specified purpose.
• the polymorphisms and genes, and corresponding marker probes, amplicons or primers described above can be embodied in any system herein, either in the form of physical nucleic acids, or in the form of system instructions that include sequence information for the nucleic acids.
• the system can include primers or amplicons corresponding to (or that amplify a portion of) a gene or polymorphism described herein.
• the set of marker probes or primers optionally detects a plurality of polymorphisms in a plurality of said genes or genetic loci.
• the set of marker probes or primers detects at least one polymorphism in each of these genes, or any other polymorphism, gene or locus defined herein.
• Any such probe or primer can include a nucleotide sequence of any such polymorphism or gene, or a complementary nucleic acid thereof, or a transcribed product thereof (e.g., a nRNA or mRNA form produced from a genomic sequence, e.g., by transcription or splicing).
• Receiveiver operating characteristic curves refer to a graphical plot of the sensitivity vs. (1 - specificity) for a binary classifier system as its discrimination threshold is varied.
• TPR true positive rate
• FPR false positive rate
• TPR & FPR two operating characteristics
• the term "combining the genetic risk assessment with the clinical risk assessment to obtain the risk” refers to any suitable mathematical analysis relying on the results of the two assessments. For example, the results of the clinical risk assessment and the genetic risk assessment may be added, more preferably multiplied.
• routine screening can include fecal occult screening, colonoscopy or sigmoidoscopy every one to two years. Various other time intervals for routine screening are discussed below.
• the methods of the present disclosure relate to assessing the risk of a subject for developing colorectal cancer by performing a genetic risk assessment.
• the genetic risk assessment is performed by analysing the genotype of the subject at two or more loci for single nucleotide polymorphisms. For example, at least 28 single nucleotide polymorphisms can be detected. In other examples, at least 29, at least 30, at least 31, at least 32, at least 33, at least 34, at least 35, at least 36, at least 37, at least 38, at least 39, at least 40, at least 41, at least 42, at least 43, at least 44 single nucleotide polymorphisms are detected. In another example, at least 45 single nucleotide polymorphisms are detected.
• each SNP which increases the risk of developing colorectal cancer has an odds ratio of association with colorectal cancer of greater than 1.0.
• none of the polymorphisms have an odds ratio of association with colorectal cancer greater than 3 or greater than 4.
• detected SNPs are selected from Table 1 or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof. In an example, at least 28 SNPs from Table 1 or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof are detected when performing the genetic risk assessment.
• the table indicates the SNP nomenclature, the gene(s) closest to or within the likely regulatory target of the SNP, the reported risk allele genotype, the reported risk allele frequency in controls, the reported association with colorectal cancer per risk allele (odds ratio), the familial relative risk (FRR) attributable to the SNP, and the proportion of the log FRR due to the SNP.
• single nucleotide polymorphisms in linkage disequilibrium with one or more of the single nucleotide polymorphisms selected from Table 1 have LD values of at least 0.5, at least 0.6, at least 0.7, at least 0.8. In another example, single nucleotide polymorphisms in linkage disequilibrium have LD values of at least 0.9. In another example, single nucleotide polymorphisms in linkage disequilibrium have LD values of at least 1.
• the genetic risk assessment may comprise detecting rs3987, rs35509282 and rs744166, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.
• the genetic risk assessment can comprise detecting rs72647484, rsl0911251, rs6687758, rsl 1903757, rs812481, rs35360328, rsl0936599, rs3987, rs35509282, rs647161, rsl321311, rsl6892766, rs6983267, rs719725, rsl0904849, rsl0795668, rs704017, rsl 1190164, rsl2241008, l lqhap (any one or all of rsl74537, rs4246215, rsl74550, and rsl535), rs3824999, rs3802842, rs3217810, rs3217901, rsl0774214, rsl l l69552, rs7136702, r
• the genetic risk assessment comprises detecting the presence of single nucleotide polymorphism rs5934683, or a single nucleotide polymorphism in linkage disequilibrium thereof.
• the number of SNPs assessed is based on the net reclassification improvement in risk prediction calculated using net reclassification index (NRI) (Pencina et al., 2008). In an embodiment, the net reclassification improvement of the methods of the present disclosure is greater than 0.01.
• the net reclassification improvement of the methods of the present disclosure is greater than 0.05. In yet another embodiment, the net reclassification improvement of the methods of the present disclosure is greater than 0.1.
• SNPs in linkage disequilibrium with those specifically mentioned herein are easily identified by those of skill in the art.
• Examples of such SNPs include four perfectly correlated SNPs within l lql2.2 (rsl74537, rs4246215, rsl74550, and rsl535). These four SNPs are named in the present disclosure as the l lql2.2 haplotype.
• Another example includes rsl 800469 and rs2241714 which are located within 19ql3.2. These SNPs are also perfectly correlated and are named in the present disclosure as the 19ql3.2 haplotype.
• rs6687758 and rs6691170 located within lq41; rsl0505477, rs6983267 and rs7014346, located within 8q24.21; rsl l632715 and rsl6969681 located within 15q31; rsl035209, rsl l l90164 located within 10q24.2; rsl l l69552, rs7136702 located within 12ql3.13 (further possible examples provided in Table 2).
• Table 2 List of SNPs (correlated SNPs) in LD* with the top six risk SNPs (DbSNP). SNPs with an r greater than 0.08 (African American, American, Asian, and European populations) in the HAPMAP dataset (http://hapmap.ncbi.nlm.nih.gov) are shown.
• the methods of the present disclosure can comprise performing a clinical risk assessment of the subject.
• the results of the clinical risk assessment can be combined with the genetic risk assessment to obtain the risk of the subject for developing colorectal cancer.
• the clinical risk assessment does not involve genotyping the subject at one or more loci. Nonetheless, the clinical risk assessment procedure may include obtaining information on mutations in the MLH1, MSH2 and MSH6 genes and micro satellite instability status.
• the clinical risk assessment procedure includes obtaining information from the subject on one or more of the following: medical history of colorectal cancer and/or polyps, age, family history of colorectal cancer and/or polyps and/or other cancer including the age of the relative at the time of diagnosis, results of previous colonoscopy and/or sigmoidoscopy, results of previous faecal occult blood test, weight, body mass index, height, sex, alcohol consumption history, smoking history, exercise history, diet (e.g. consumption of folate, vegetables, red meat, fruits, fibre, and saturated fats), prevalence of inflammatory bowel disease, race/ethnicity, aspirin and NSAID use, implementation of estrogen replacement and use of oral contraceptives.
• medical history of colorectal cancer and/or polyps age, family history of colorectal cancer and/or polyps and/or other cancer including the age of the relative at the time of diagnosis, results of previous colonoscopy and/or sigmoidoscopy, results of previous f
• the clinical risk assessment procedure can include obtaining information from the subject on first degree relative's history of colorectal cancer.
• the clinical risk assessment procedure includes obtaining information from the subject on age and/or first degree relative's history of colorectal cancer.
• the clinical risk assessment includes details regarding the family history of colorectal cancer of at least some, preferably all, first degree relatives.
• family history of colorectal cancer involves an analysis of multigenerational family history.
• multigenerational family history refers to the analysis of 2 or more generations. Multigenerational family history may include an analysis of, for instance, across the same generation (for example cousins), and/or between generations (for example uncles and aunts).
• the clinical risk assessment includes details regarding the family history of colorectal cancer of at least some, preferably all, second degree relatives. In another embodiment, the clinical risk assessment includes details regarding the family history of colorectal cancer of at least some, preferably all, second and third degree relatives.
• the clinical risk assessment procedure provides an estimate of the risk of the subject developing colorectal cancer during the next 5-year period (i.e. 5-year risk).
• the 5-year risk determined by the clinical risk assessment is between about 1% to about 3%.
• the 5-year risk determined by the clinical risk assessment is between about 1.5% to about 2%.
• the clinical risk assessment procedure provides an estimate of the risk of the subject developing colorectal cancer during the next 10-year period (i.e. 10-year risk).
• the 10-year risk determined by the clinical risk assessment is between about 1% to about 3%.
• the 5-year risk determined by the clinical risk assessment is between about 1.5% to about 2%.
• the clinical risk assessment procedure provides an estimate of the risk of the subject developing colorectal cancer up to age 70 (i.e. lifetime risk).
• lifetime risk determined by the clinical risk assessment is between about 15% to about 30%. In another example, the lifetime determined by the clinical risk assessment is between about 20% to about 25%.
• performing the clinical risk assessment uses a model which calculates the absolute risk of developing colon cancer.
• the absolute risk of developing colon cancer can be calculated using cancer incidence rates while accounting for the competing risk of dying from other causes apart from colon cancer.
• the clinical risk assessment provides a 5-year absolute risk of developing colon cancer.
• the clinical risk assessment provides a 10-year absolute risk of developing colon cancer.
• clinical risk assessment procedures include, but are not limited to, the Harvard Cancer Risk Index, the National Cancer Institute's Colorectal Cancer Risk Assessment Tool, the Cleveland Clinic Tool, the Mismatch Repair probability model (also known as MMRpro), Colorectal Risk Prediction Tool (CRiPT) and the like (see, for example, Usher-Smith et al., 2015).
• MMRpro Mismatch Repair probability model
• CRiPT Colorectal Risk Prediction Tool
• the Harvard Cancer Risk Index predicts a 10 year risk of developing colon cancer using family history data (first degree relatives with colon cancer), and environmental factors such as body mass index, aspirin use, cigarette smoking, history of inflammatory bowel disease, height, physical activity, estrogen replacement, use of oral contraceptives, and consumption of folate, vegetables, alcohol, red meat, fruits, fibre, and saturated fats.
• family history data first degree relatives with colon cancer
• environmental factors such as body mass index, aspirin use, cigarette smoking, history of inflammatory bowel disease, height, physical activity, estrogen replacement, use of oral contraceptives, and consumption of folate, vegetables, alcohol, red meat, fruits, fibre, and saturated fats.
• the clinical risk assessment procedure uses the Harvard Cancer Risk Index to predict the 10 year risk of the subject developing colon cancer.
• the Colorectal Cancer Risk Assessment Tool predicts 5-, 10-, 20-year, and lifetime risks of developing colorectal cancer for people over 50 years of age based on age, sex, use of sigmoidoscopy and/or colonoscopy, current leisure time activity, use of aspirin and NSAIDs, history of cigarette smoking, body mass index, history of hormone replacement, and consumption of vegetables.
• the clinical risk assessment procedure uses the Colorectal Cancer Risk Assessment Tool to predict the 5 year risk of the subject developing colorectal cancer.
• the clinical risk assessment procedure uses the Colorectal Cancer Risk Assessment Tool to predict the 10 year risk of the subject developing colorectal cancer.
• the clinical risk assessment procedure uses the Colorectal Cancer Risk Assessment Tool to predict the 20 year risk of the subject developing colorectal cancer. In another example, the clinical risk assessment procedure uses the Colorectal Cancer Risk Assessment Tool to predict the lifetime risk of the subject developing colorectal cancer.
• the Cleveland Clinic Tool provides a colorectal cancer risk score based on age, sex, ethnicity, weigth, height, use of sigmoidoscopy and/or colonoscopy, faecal occult blood test, cigarette smoking, exercise, history of colorectal cancer and polyps, and consumption of vegetables and fruits.
• the MMRpro model predicts five year and lifetime risks of developing colorectal and endometrial cancer based on mutations in the MLHl, MSH2 and MSH6 genes, as well as environmental factors such as family history of the disease, micro satellite instability status, age, and ethnicity.
• the clinical risk assessment procedure uses the MMRpro model to predict the 5 year risk of the subject developing colorectal cancer.
• the clinical risk assessment procedure uses the MMRpro model to predict the lifetime risk of the subject developing colorectal cancer.
• the Colorectal Risk Prediction Tool (CRiPT) model uses multi-generational family history using a mixed major gene polygenic model to estimate colorectal cancer risk.
• An individual's “genetic risk” can be defined as the product of genotype relative risk values for each SNP assessed.
• a log-additive risk model can then be used to define three genotypes AA, AB, and BB for a single SNP having relative risk values of 2
• OR is the previously reported disease odds ratio for the high-risk allele, B, vs the low-risk allele, A. If the B allele has frequency (p), then these genotypes have population frequencies of (1 - p) , 2p(l - p), and p , assuming Hardy-Weinberg equilibrium. The genotype relative risk values for each SNP can then be scaled so that based on these frequencies the average relative risk in the population is 1. Specifically, given the unsealed population average relative risk:
• Adjusted risk values 1/ ⁇ , OR/ ⁇ , and OR / ⁇ are used for AA, AB, and BB genotypes. Missing genotypes are assigned a relative risk of 1.
• the following formula can be used to define the genetic risk:
• PRS ⁇ ⁇ ⁇ + ⁇ 2 ⁇ 2 + . . . . ⁇ ⁇ ⁇ + ⁇ ⁇ ⁇ ⁇
• ⁇ ⁇ is the per-allele log odds ratio (OR) for colon cancer associated with the minor allele for SNP ⁇
• x K the number of alleles for the same SNP (0, 1 or 2)
• n is the total number of SNPs
• PRS is the polygenic risk score (which can also be referred to as composite SNP risk).
• the "risk" of a human subject for developing colorectal cancer can be provided as a relative risk (or risk ratio) or an absolute risk as required.
• the genetic risk assessment obtains the "relative risk" of a human subject for developing colorectal cancer.
• Relative risk or risk ratio
• Relative risk is helpful to identify characteristics that are associated with a disease, but by itself is not particularly helpful in guiding screening decisions because the frequency of the risk (incidence) is cancelled out.
• the genetic risk assessment obtains the "absolute risk" of a human subject for developing colorectal cancer.
• Absolute risk is the numerical probability of a human subject developing colorectal cancer within a specified period (e.g. 5, 10, 15, 20 or more years). It reflects a human subject's risk of developing colorectal cancer in so far as it does not consider various risk factors in isolation.
• SNPi to SNP 4 5 are the relative risk for the individual SNPs, each scaled to have a population average of 1 as outlined above. Because the SNP risk values have been "centred" to have a population average risk of 1, if one assumes independence among the SNPs, then the population average risk across all genotypes for the combined value is consistent with the underlying Clinical Evaluation risk estimate.
• the risk of a human subject for developing colorectal cancer is calculated by [Clinical Evaluation risk] x SNPi x SNP 2 x SNP 3 x SNP 4 x SNP 5 x SNP 6 x SNP 7 ,x SNP 8i ... x SNP 4 5 etc.
• the risk of a human subject for developing colorectal cancer is calculated by [Clinical Evaluation 5-year risk] x SNPi x SNP 2 x SNP 3 x SNP 4 x SNP 5 x SNP 6 x SNP 7 ,x SNP 8 , ... x SNP 45 etc.
• the risk of a human subject for developing colorectal cancer is calculated by [Clinical Evaluation lifetime risk] x SNPi x SNP 2 x SNP 3 x SNP 4 x SNP 5 x SNP 6 x SNP 7 ,x SNP 8 , ... x SNP 45 etc.
• the Clinical Evaluation is performed by assessing one or more of the following: medical history of colorectal cancer, age, family history of colorectal cancer, results of previous colonoscopy/sigmoidoscopy and race/ethnicity to provide a clinical risk.
• the risk i.e. combined genetic risk x clinical risk
• the Clinical Evaluation is performed by assessing first degree relatives history of colorectal cancer to provide a clinical risk.
• the risk i.e. combined genetic risk x clinical risk
• the risk is provided by:
• the proportion of log familial relative risk (FRR; the odds ratio for colorectal cancer associated with having a first-degree relative with colorectal cancer) that could be attributable to the risk alleles of the SNPs can be estimated (assuming detection of 45 SNPs, Hardy-Weinberg equilibrium for each SNP, linkage equilibrium between the SNPs, and a multiplicative model for the associations of the SNPs with colorectal cancer risk).
• SNPi,... SNP 4 5 are SNPs from Table 1 and clinical 4 6,...clinical m are clinical factors (note: these could be any heritable factors contributing to the FRR).
• Gi is a random variable giving the number of risk alleles at SNPi for a random person from the population
• the proportion of the log FRR due to the known SNPs is 1 ⁇ 2[Var(Xi)+...+Var(X 4 5)/logFRR, while the proportion due to clinical factor(s) is one minus this value. Additional clinical factors can be incorporated into the above calculation as required.
• the genetic risk assessment is combined with the clinical risk assessment to obtain the "relative risk” of a human subject for developing colorectal cancer. In another embodiment, the genetic risk assessment is combined with the clinical risk assessment to obtain the "absolute risk” of a human subject for developing colorectal cancer. Subjects
• subject refers to a human subject.
• terms such as “subject”, “patient” or “individual” are terms that can, in context, be used interchangeably in the present disclosure.
• the methods of the present disclosure can be used for routine screening of subjects. Routine screening can include testing subjects at pre-determined time intervals. Exemplary time intervals include screening monthly, quarterly, six monthly, yearly, every two years or every three years.
• subjects screened using the methods of the present disclosure are at least 38, at least 39, at least 40, at least 41, at least 42, at least 43, at least 44, at least 45, at least 46, at least 47, at least 48, at least 49 years of age.
• the subject is at least 40 years of age.
• Subjects that have a family history of colorectal cancer can be screened earlier. For example, these subjects can be screened from at least 30, at least 31, at least 32, at least 33, at least 34, at least 35, at least 36, at least 37 years of age or older.
• subjects assessed using the methods of the present disclosure have had a positive fecal occult blood test.
• subjects have a personal history of adenomatous polyps or a personal history of inflammatory bowel disease (ulcerative colitis or Crohn's disease).
• the methods of the present disclosure can be used to assess the risk of a human subject for developing colorectal cancer with symptoms that may be indicative of colorectal cancer.
• the present disclosure would be applicable to a subject with a positive fecal occult screening test or a subject presenting to the clinic with symptoms such change in bowel habits, including diarrhea or constipation, change in the stool consistency, rectal bleeding, persistent abdominal discomfort, such as cramps, incomplete bowel movement, gas or pain.
• the methods of the present disclosure can be used to assess risk in male and female subjects.
• the subject is male.
• the methods of the present disclosure can be used for assessing the risk for developing colorectal cancer in human subjects from various ethnic backgrounds. It is well known that over time there has been blending of different ethnic origins. While in practice, this does not influence the ability of a skilled person to practice the methods described herein, it may be desirable to identify the subject's ethnic background.
• the ethnicity of the human subject can be self-reported by the subject. As an example, subjects can be asked to identify their ethnicity in response to this question: "To what ethnic group do you belong?" In another example, the ethnicity of the subject can be derived from medical records after obtaining the appropriate consent from the subject or from the opinion or observations of a clinician.
• the subject can be classified as Caucasoid, Australoid, Mongoloid and Negroid based on physical anthropology.
• the subject can be Caucasian, African American, Hispanic, Asian, Indian, or Latino.
• the subject is Caucasian.
• the subject can be European.
• a subject of predominantly European origin, either direct or indirect through ancestry, with white skin is considered Caucasian in the context of the present disclosure.
• a Caucasian may have, for example, at least 75% Caucasian ancestry (for example, but not limited to, the subject having at least three Caucasian grandparents).
• Negroid A subject of predominantly central or southern African origin, either direct or indirect through ancestry, is considered Negroid in the context of the present disclosure.
• a Negroid may have, for example, at least 75% Negroid ancestry.
• An American subject with predominantly Negroid ancestry and black skin is considered African American in the context of the present disclosure.
• An African American may have, for example, at least 75% Negroid ancestry. Similar principle applies to, for example, subjects of Negroid ancestry living in other countries (for example Great Britain, Canada or the Netherlands).
• Hispanic A subject predominantly originating from Spain or a Spanish-speaking country, such as a country of Central or Southern America, either direct or indirect through ancestry, is considered Hispanic in the context of the present disclosure.
• a Hispanic subject may have, for example, at least 75% Hispanic ancestry.
• Fecal occult blood testing and colonoscopy/ sigmoidoscopy reduces mortality from colorectal cancer but are expensive to routinely offer to large numbers of subjects. Accordingly, identifying the right population to screen is desirable.
• the methods of the present disclosure can be used for determining the need for routine diagnostic testing of a human subject for colorectal cancer. Such routine screening can include either fecal occult blood testing or colonoscopy/sigmoidoscopy at pre- determined time intervals such as those discussed above.
• the need for routine diagnostic testing of a human subject for colorectal cancer is determined based on the number risk alleles detected.
• each of the single nucleotide polymorphisms may be present up to twice in the somatic diploid genome of the subject.
• an assessment of 28 single nucleotide polymorphisms may result in the detection of 56 alleles.
• an assessment of 45 single nucleotide polymorphisms may result in the detection of 90 alleles.
• a proportion of the detected alleles may be risk alleles. The number of risk alleles detected is relevant for the subject's risk of developing a colon cancer.
• a subject having at least 41, at least 42, at least 43, at least 44, at least 45, at least 46, at least 47, at least 48, at least 49, at least 50, at least 51, at least 52, at least 53, at least 54, at least 55, at least 56, at least 57, at least 58, at least 59, at least 60 or more risk alleles of the single nucleotide polymorphisms should be enrolled in a fecal occult screening, colonoscopic or sigmoidoscopic screening program.
• subjects with at least 44 risk alleles of the single nucleotide polymorphisms should be enrolled in a fecal occult screening, colonoscopic or sigmoidoscopic screening program.
• subjects at least 49 years of age with at least 44 risk alleles of the single nucleotide polymorphisms should be enrolled in a colonoscopic or sigmoidoscopic screening program.
• subjects with at least 46 risk alleles of the single nucleotide polymorphisms should be enrolled in a fecal occult screening, colonoscopic or sigmoidoscopic screening program.
• subjects at least 47 years of age with at least 46 risk alleles of the single nucleotide polymorphisms should be enrolled in a colonoscopic or sigmoidoscopic screening program.
• the need for routine diagnostic testing of a human subject for colorectal cancer is determined based on the subjects risk ranking within a population of subjects. For example, if the assessment places the subject in the top 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1% of subjects in a population at risk of developing colorectal cancer, then the subject is enrolled in a fecal occult screening, colonoscopic or sigmoidoscopic screening program.
• the genetic risk is calculated based on: SNPi x SNP 2 x SNP 3 x SNP 4 x SNP 5 x SNP 6 x SNP 7 ,x SNP X and subjects having a risk greater than about 5.9% are enrolled in a fecal occult screening, colonoscopic or sigmoidoscopic screening program. In another example, subjects having a risk greater than about 6.0, 6.1, 6.2, 6.3, 6.4, 6.5, 6.5, 6.6, 6.7, 6.8, 6.9, 7.0, 7.1, 7.2, 7.3, 7.4% or more are enrolled in a fecal occult screening, colonoscopic or sigmoidoscopic screening program.
• the combined risk (i.e. clinical x genetic risk) is calculated based on: [clinical risk associated with a having a first degree relative with colorectal cancer] x SNPi x SNP 2 x SNP 3 x SNP 4 x SNP 5 x SNP 6 x SNP 7 ,x SNP X and subjects having a risk greater than about 11.5% are enrolled in a fecal occult screening, colonoscopic or sigmoidoscopic screening program. In another example, subjects having a risk greater than about 12, 12.5, 13, 13.1, 13.2, 13.3, 13.4, 13.5, 14% or more are enrolled in a fecal occult screening, colonoscopic or sigmoidoscopic screening program.
• the methods of the present disclosure are incorporated into a method of screening for colorectal cancer in a subject.
• the risk of a subject for developing colorectal cancer is assessed using the methods of the present disclosure and the subject is routinely screened for colorectal cancer via colonoscopy or sigmoidoscopy if they are assessed as having a risk for developing colorectal cancer.
• the methods of the present disclosure can also be used in combination with other methods or "additional test(s)" in providing an evaluation of the risk of developing colorectal cancer.
• results of multiple tests may assist a clinician in determining whether a more definitive test such as a colonoscopy or sigmoidoscopy is required.
• the methods of the present disclosure are performed in combination with a fecal occult blood test.
• the method performance is characterized by an area under the curve (AUC) of at least about 0.61, at least about 0.62, at least about 0.63.
• AUC area under the curve
• the sensitivity achieved by the methods of the present disclosure is at least about 50%, at least about 60%, at least about 70%, at least about 71%, at least about 72%, at least about 73%, at least about 74%, at least about 75%, at least about 76%, at least about 77%, at least about 78%, at least about 79%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%.
• the specificity achieved by the methods of the present disclosure is at least about 50%, at least about 60%, at least about 70%, at least about 71%, at least about 72%, at least about 73%, at least about 74%, at least about 75%, at least about 76%, at least about 77%, at least about 78%, at least about 79%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%.
• a high genetic propensity for colorectal cancer can be treated as a warning to commence prophylactic or therapeutic treatment.
• treatment may be prescribed or administered to the subject.
• the methods of the present disclosure relate to an anti-colorectal cancer therapy for use in preventing or reducing the risk of colorectal cancer in a human subject at risk thereof.
• the subject may be prescribed or administered a therapeutic or prophylactic agent.
• the subject may be prescribed or administered a chemopreventative.
• the subject may be prescribed or administered nonsteroidal anti-inflammatory drug(s) such as aspirin, buprofen, acetaminophen, and naproxen or hormone therapy (estrogen plus progestin).
• treatment may include behavioural intervention such as manipulation of the subjects diet. Exemplary dietary modifications include increased fibre, mono-saturated fatty acids and/or fish oil.
• sample and “specimen” are terms that can, in context, be used interchangeably in the present disclosure.
• Any biological material can be used as the above-mentioned sample so long as it can be derived from the subject and DNA can be isolated and analyzed according to the methods of the present disclosure.
• Samples are typically taken, following informed consent, from a patient by standard medical laboratory methods. The sample may be in a form taken directly from the patient, or may be at least partially processed (purified) to remove at least some non-nucleic acid material.
• biological samples include bodily fluids (blood, saliva, urine etc.), biopsy, tissue, and/or waste from the patient.
• tissue biopsies, stool, sputum, saliva, blood, lymph, tears, sweat, urine, vaginal secretions, or the like can easily be screened for SNPs, as can essentially any tissue of interest that contains the appropriate nucleic acids.
• the biological sample is a cheek cell sample.
• the sample is a blood sample.
• a blood sample can be treated to remove particular cells using various methods such as such centrifugation, affinity chromatography (e.g. immunoabsorbent means), immunoselection and filtration if required.
• the sample can comprise a specific cell type or mixture of cell types isolated directly from the subject or purified from a sample obtained from the subject.
• the biological sample is peripheral blood mononuclear cells (pBMC).
• pBMC peripheral blood mononuclear cells
• pBMC peripheral blood mononuclear cells
• pBMC peripheral blood mononuclear cells
• pBMC peripheral blood mononuclear cells
• DNA can be extracted from the sample for detecting SNPs.
• the DNA is genomic DNA.
• genomic DNA Various methods of isolating DNA, in particular genomic DNA are known to those of skill in the art. In general, known methods involve disruption and lysis of the starting material followed by the removal of proteins and other contaminants and finally recovery of the DNA. For example, techniques involving alcohol precipitation; organic phenol/chloroform extraction and salting out have been used for many years to extract and isolate DNA.
• There are various commercially available kits for genomic DNA extraction Qiagen, Life technologies; Sigma). Purity and concentration of DNA can be assessed by various methods, for example, spectrophotometry.
• Amplification primers for amplifying markers can be used in the disclosure.
• markers e.g., marker loci
• suitable probes to detect such markers or to genotype a sample with respect to multiple marker alleles can be used in the disclosure.
• primer selection for long-range PCR is described in US 10/042,406 and US 10/236,480; for short-range PCR, US 10/341,832 provides guidance with respect to primer selection.
• there are publicly available programs such as "Oligo" available for primer design. With such available primer selection and design software, the publicly available human genome sequence and the polymorphism locations, one of skill in the art can construct primers to amplify the SNPs to practice the disclosure.
• the precise probe to be used for detection of a nucleic acid comprising a SNP can vary, e.g., any probe that can identify the region of a marker amplicon to be detected can be used in conjunction with the present disclosure.
• the configuration of the detection probes can, of course, vary.
• oligonucleotide primers useful for amplifying nucleic acids comprising SNPs known to be associated with a colorectal cancer are provided in Table 3.
• the sequence of the genomic region to which these oligonucleotides hybridize can be used to design primers which are longer at the 5' and/or 3' end, possibly shorter at the 5' and/or 3' (as long as the truncated version can still be used for amplification), which have one or a few nucleotide differences (but nonetheless can still be used for amplification), or which share no sequence similarity with those provided but which are designed based on genomic sequences close to where the specifically provided oligonucleotides hybridize and which can still be used for amplification.
• the primers of the disclosure are radiolabeled, or labelled by any suitable means (e.g., using a non-radioactive fluorescent tag), to allow for rapid visualization of differently sized amplicons following an amplification reaction without any additional labelling step or visualization step.
• the primers are not labelled, and the amplicons are visualized following their size resolution, e.g., following agarose or acrylamide gel electrophoresis.
• ethidium bromide staining of the PCR amplicons following size resolution allows visualization of the different size amplicons.
• the primers of the disclosure be limited to generating an amplicon of any particular size.
• the primers used to amplify the marker loci and alleles herein are not limited to amplifying the entire region of the relevant locus, or any subregion thereof.
• the primers can generate an amplicon of any suitable length for detection.
• marker amplification produces an amplicon at least 20 nucleotides in length, or alternatively, at least 50 nucleotides in length, or alternatively, at least 100 nucleotides in length, or alternatively, at least 200 nucleotides in length.
• Amplicons of any size can be detected using the various technologies described herein. Differences in base composition or size can be detected by conventional methods such as electrophoresis.
• amplification is not a requirement for marker detection, for example one can directly detect unamplified genomic DNA simply by performing a Southern blot on a sample of genomic DNA.
• molecular markers are detected by any established method available in the art, including, without limitation, allele specific hybridization (ASH), detection of single nucleotide extension, array hybridization (optionally including ASH), or other methods for detecting single nucleotide polymorphisms, amplified fragment length polymorphism (AFLP) detection, amplified variable sequence detection, randomly amplified polymorphic DNA (RAPD) detection, restriction fragment length polymorphism (RFLP) detection, self-sustained sequence replication detection, simple sequence repeat (SSR) detection, and single-strand conformation polymorphisms (SSCP) detection.
• ASH allele specific hybridization
• RAPD randomly amplified polymorphic DNA
• RFLP restriction fragment length polymorphism
• SSR simple sequence repeat
• SSCP single-strand conformation polymorphisms
• Some techniques for detecting genetic markers utilize hybridization of a probe nucleic acid to nucleic acids corresponding to the genetic marker (e.g., amplified nucleic acids produced using genomic DNA as a template).
• Hybridization formats including, but not limited to: solution phase, solid phase, mixed phase, or in situ hybridization assays are useful for allele detection. An extensive guide to the hybridization of nucleic acids is found in Tijssen (1993) and Sambrook et al. (supra).
• PCR detection using dual-labelled fluorogenic oligonucleotide probes can also be performed according to the present disclosure.
• These probes are composed of short (e.g., 20-25 bases) oligodeoxynucleotides that are labelled with two different fluorescent dyes. On the 5' terminus of each probe is a reporter dye, and on the 3' terminus of each probe a quenching dye is found.
• the oligonucleotide probe sequence is complementary to an internal target sequence present in a PCR amplicon. When the probe is intact, energy transfer occurs between the two fluorophores and emission from the reporter is quenched by the quencher by FRET.
• TaqManTM probes are oligonucleotides that have a label and a quencher, where the label is released during amplification by the exonuclease action of the polymerase used in amplification. This provides a real time measure of amplification during synthesis.
• TaqManTM reagents are commercially available, e.g., from Applied Biosystems (Division Headquarters in Foster City, Calif.) as well as from a variety of specialty vendors such as Biosearch Technologies (e.g., black hole quencher probes). Further details regarding dual-label probe strategies can be found, e.g., in WO 92/02638. Other similar methods include e.g. fluorescence resonance energy transfer between two adjacently hybridized probes, e.g., using the "LightCycler®" format described in US 6,174,670.
• Array-based detection can be performed using commercially available arrays, e.g., from Affymetrix (Santa Clara, Calif.) or other manufacturers. Reviews regarding the operation of nucleic acid arrays include Sapolsky et al. (1999); Lockhart (1998); Fodor (1997a); Fodor (1997b) and Chee et al. (1996). Array based detection is one preferred method for identification markers of the disclosure in samples, due to the inherently high-throughput nature of array based detection.
• the nucleic acid sample to be analyzed is isolated, amplified and, typically, labelled with biotin and/or a fluorescent reporter group.
• the labelled nucleic acid sample is then incubated with the array using a fluidics station and hybridization oven.
• the array can be washed and or stained or counter-stained, as appropriate to the detection method. After hybridization, washing and staining, the array is inserted into a scanner, where patterns of hybridization are detected.
• the hybridization data are collected as light emitted from the fluorescent reporter groups already incorporated into the labelled nucleic acid, which is now bound to the probe array. Probes that most clearly match the labelled nucleic acid produce stronger signals than those that have mismatches. Since the sequence and position of each probe on the array are known, by complementarity, the identity of the nucleic acid sample applied to the probe array can be identified.
• Correlations between SNPs and risk of colorectal cancer can be performed by any method that can identify a relationship between an allele and increased cancer risk, or a combination of alleles and increased cancer risk.
• alleles in genes or loci defined herein can be correlated with increased risk of colorectal cancer.
• these methods involve referencing a look up table that comprises correlations between alleles of the polymorphism and the cancer risk.
• the table can include data for multiple allele-risk relationships and can take account of additive or other higher order effects of multiple allele-risk relationships, e.g., through the use of statistical tools such as principle component analysis, heuristic algorithms, etc.
• Correlation of a marker to a cancer risk optionally includes performing one or more statistical tests for correlation. Many statistical tests are known, and most are computer- implemented for ease of analysis. A variety of statistical methods of determining associations/correlations between phenotypic traits and biological markers are known and can be applied to the present disclosure. Haiti (1981). A variety of appropriate statistical models are described in Lynch and Walsh (1998). These models can, for example, provide for correlations between genotypic and phenotypic values, characterize the influence of a locus on cancer risk, sort out the relationship between environment and genotype, determine dominance or penetrance of genes, determine maternal and other epigenetic effects, determine principle components in an analysis (via principle component analysis, or "PCA"), and the like. The references cited in these texts provide considerable further detail on statistical models for correlating markers and cancer risk.
• PCA principle component analysis
• neural network approaches can be coupled to genetic algorithm- type programming for heuristic development of a structure-function data space model that determines correlations between genetic information and phenotypic outcomes.
• any statistical test can be applied in a computer implemented model, by standard programming methods, or using any of a variety of "off the shelf” software packages that perform such statistical analyses, including, for example, those noted above and those that are commercially available, e.g., from Partek Incorporated (St. Peters, Mo.; www.partek.com), e.g., that provide software for pattern recognition (e.g., which provide Partek Pro 2000 Pattern Recognition Software).
• system instructions that correlate the presence or absence of an allele (whether detected directly or, e.g., through expression levels) with a predicted cancer risk.
• the system instructions can also include software that accepts diagnostic information associated with any detected allele information, e.g., a diagnosis that a subject with the relevant allele has a particular cancer risk.
• This software can be heuristic in nature, using such inputted associations to improve the accuracy of the look up tables and/or interpretation of the look up tables by the system. A variety of such approaches, including neural networks, Markov modelling and other statistical analysis are described above.
• the disclosure provides methods of determining the polymorphic profile of an individual at the SNPs outlined in the present disclosure (Table 6) or SNPs in linkage disequilibrium with one or more thereof.
• the polymorphic profile constitutes the polymorphic forms occupying the various polymorphic sites in an individual.
• two polymorphic forms usually occupy each polymorphic site.
• the polymorphic profile at sites X and Y can be represented in the form X (xl, xl), and Y (yl, y2), wherein xl, xl represents two copies of allele xl occupying site X and yl, y2 represent heterozygous alleles occupying site Y.
• the polymorphic profile of an individual can be scored by comparison with the polymorphic forms associated with susceptibility to colorectal cancer occurring at each site.
• the comparison can be performed on at least, e.g., 1, 2, 5, 10, 25, 50, or all of the polymorphic sites, and optionally, others in linkage disequilibrium with them.
• the polymorphic sites can be analyzed in combination with other polymorphic sites.
• Polymorphic profiling is useful, for example, in selecting agents to affect treatment or prophylaxis of colorectal cancer in a given individual. Individuals having similar polymorphic profiles are likely to respond to agents in a similar way.
• the methods of the present disclosure may be implemented by a system as a computer implemented method.
• the system may be a computer system comprising one or a plurality of processors which may operate together (referred to for convenience as "processor") connected to a memory.
• the memory may be a non- transitory computer readable medium, such as a hard drive, a solid state disk or CD- ROM.
• Software that is executable instructions or program code, such as program code grouped into code modules, may be stored on the memory, and may, when executed by the processor, cause the computer system to perform functions such as determining that a task is to be performed to assist a user to determine the risk of a human subject for developing colorectal cancer receiving data indicating the genetic risk and optionally the clinical risk of the subject developing colorectal cancer, wherein the genetic risk was derived by detecting, in a biological sample derived from the subject, the presence of at least 28 single nucleotide polymorphisms shown in Table 1 or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof; processing the data to obtain the risk of a human subject for developing colorectal cancer; outputting the presence of the risk of a human subject for developing colorectal cancer.
• the memory may comprise program code which when executed by the processor causes the system to determine the presence of at least 28 single nucleotide polymorphisms selected from Table 1, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof, or receive data indicating the presence of at least 28 single nucleotide polymorphisms selected from Table 1, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof; process the data to obtain the risk of a human subject for developing colorectal cancer; report the risk of a human subject for developing colorectal cancer.
• the program code causes the system to determine the "genetic risk".
• the memory may comprise program code which when executed by the processor causes the system to determine the presence of at least 28 single nucleotide polymorphisms selected from Table 1, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof, or receive data indicating the presence of at least 28 single nucleotide polymorphisms selected from Table 1, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof and, receive or determine clinical risk data for the subject; process the data to combine the genetic risk data with the clinical risk data to obtain the risk of the subject for developing colorectal cancer; report the risk of a human subject for developing colorectal cancer.
• the program code can cause the system to combine clinical risk assessment data x genetic risk.
• the system may be coupled to a user interface to enable the system to receive information from a user and/or to output or display information.
• the user interface may comprise a graphical user interface, a voice user interface or a touchscreen.
• the user interface is a SNP array platform.
• the system may be configured to communicate with at least one remote device or server across a communications network such as a wireless communications network.
• a communications network such as a wireless communications network.
• the system may be configured to receive information from the device or server across the communications network and to transmit information to the same or a different device or server across the communications network.
• the system may be isolated from direct user interaction.
• performing the methods of the present disclosure to assess the risk of a subject for developing colorectal cancer enables establishment of a diagnostic or prognostic rule based on the the genetic risk of the subject developing colorectal cancer.
• the diagnostic or prognostic rule can be based on the genetic risk relative to a control, standard or threshold level of risk.
• the diagnostic or prognostic rule can be based on the combined genetic and clinical risk relative to a control, standard or threshold level of risk.
• the diagnostic or prognostic rule is based on the application of a statistical and machine learning algorithm.
• a statistical and machine learning algorithm uses relationships between a population of SNPs and disease status observed in training data (with known disease status) to infer relationships which are then used to determine the risk of a human subject for developing colorectal cancer in subjects with an unknown risk.
• An algorithm is employed which provides a risk of a human subject developing colorectal cancer. The algorithm performs a multivariate or univariate analysis function. Kits and Products
• the present disclosure provides a kit comprising at least 28 sets of primers for amplifying 28 or more nucleic acids, wherein the 28 or more nucleic acids comprise a single nucleotide polymorphism selected from Table 1, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.
• the kit comprises at least 28, at least 29, at least 30, at least
• primers can be designed to amplify the SNP as a matter of routine.
• Various software programs are freely available that can suggest suitable primers for amplifying SNPs of interest.
• PCR primers of a PCR primer pair can be designed to specifically amplify a region of interest from human DNA.
• the region of interest contains the single- base variation (e.g. single-nucleotide polymorphism, SNP) which shall be genotyped.
• SNP single-nucleotide polymorphism
• Each PCR primer of a PCR primer pair can be placed adjacent to a particular single - base variation on opposing sites of the DNA sequence variation.
• PCR primers can be designed to avoid any known DNA sequence variation and repetitive DNA sequences in their PCR primer binding sites.
• the kit may further comprise other reagents required to perform an amplification reaction such as a buffer, nucleotides and/or a polymerase, as well as reagents for extracting nucleic acids from a sample.
• reagents required to perform an amplification reaction such as a buffer, nucleotides and/or a polymerase, as well as reagents for extracting nucleic acids from a sample.
• Array based detection is one preferred method for assessing the SNPs of the disclosure in samples, due to the inherently high-throughput nature of array based detection.
• a variety of probe arrays have been described in the literature and can be used in the context of the present disclosure for detection of SNPs that can be correlated to colorectal cancer.
• DNA probe array chips are used in one embodiment of the disclosure.
• the recognition of sample DNA by the set of DNA probes takes place through DNA hybridization. When a DNA sample hybridizes with an array of DNA probes, the sample binds to those probes that are complementary to the sample DNA sequence.
• By evaluating to which probes the sample DNA for an individual hybridizes more strongly it is possible to determine whether a known sequence of nucleic acid is present or not in the sample, thereby determining whether a marker found in the nucleic acid is present.
• the present disclosure provides a genetic array comprising at least 28 sets of probes for hybridising to 28 or more nucleic acids, wherein the 28 or more nucleic acids comprise a single nucleotide polymorphism selected from Table 1, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.
• the array comprises at least 28, at least 29, at least 30, at least 31, at least 32, at least 33, at least 34, at least 35, at least 36, at least 37, at least 38, at least 39, at least 40, at least 41, at least 42, at least 43, at least 44, at least 45 probes for hybridising to nucleic acids comprising a single nucleotide polymorphism selected from Table 1, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.
• Primers and probes for other SNPs can be included with the above exemplified kits.
• primers and/or probes may be included for X chromosome SNP (rs5934683) or various other SNPs.
• SNPs associated with colorectal cancer in European populations were identified. Of these, four SNPs within l lql2.2 (rsl74537, rs4246215, rsl74550, and rsl535) are perfectly correlated and can be represented by a common haplotype (named here as the l lql2.2 haplotype). Two SNPs within 19ql3.2 (rsl800469 and rs2241714) are perfectly correlated and can be represented by a common haplotype (named here as the 19ql3.2 haplotype). One SNP is on the X chromosome (rs5934683) and was not included in the simulation of colorectal cancer risk for males and females combined.
• rs6687758 and rs6691170 Two SNPs within lq41 (rs6687758 and rs6691170) are in linkage disequilibrium. Thus, rs6691170 was excluded. Three SNPs within 8q24.21 (rsl0505477, rs6983267, and rs7014346) have a D prime of 1.0. Thus, rs 10505477 and rs7014346 were excluded. Two SNPs within 10q24.2 (rsl035209 and rsl 1190164) have a D prime of 0.9. Thus, rs 1035209 was excluded.
• SNPs have been identified in total with remaining SNPs being in linkage disequilibrium thereof or on the X chromosome.
• SNPs indicative of colorectal cancer risk are shown in Table 4.
• the allele frequency of each risk allele and the odds ratio per risk allele is also shown in Table 4.
• the average risk allele frequency was 0.43 (range 0.07 to 0.91).
• the average odds ratio per risk allele was 1.14 (range 1.05 to 1.53).
• the average familial relative risk (FRR; the odds ratio for colorectal cancer associated with having a first-degree relative with colorectal cancer) that could be attributed to each SNP was 1.0040 (range 1.0006 to 1.0281), which is 0.50% (range 0.07% to 3.41%) of the total log FRR.
• the combined FRR that could be attributable to all 45 SNPs was 1.1980, which is 22.3% of the total log FRR.
• the estimated FRR not due to the SNPs was 1.88.
• SNPs associated with colorectal cancer The table indicates the SNP nomenclature, the gene(s) closest to or within the likely regulatory target of the SNP, the reported risk allele genotype, the reported risk allele frequency in controls, the reported association with colorectal cancer per risk allele (odds ratio), the familial relative risk (FRR) attributable to the SNP, and the proportion of the log FRR due to the SNP. *Gene/s closest to or likely regulatory target of SNP. SNPs in linkage disequilibrium are shown in square brackets [ ] .
• the discriminatory power of the SNPs was assessed to distinguish cases from controls using a receiver operating curve and estimating the area under the curve (the probability that a randomly selected colorectal cancer case will have more risk alleles than a randomly selected control).
• the odds ratios was estimated for colorectal cancer risk for: (i) being in the highest and lowest quintile for the number of risk alleles being in the middle quintile; (ii) being in the highest and lowest decile for the number of risk alleles versus being in the median number of risk alleles; and (iii) per standard deviation of risk alleles. Cut-offs for number of risk alleles for quintiles and deciles, and the standard deviation, were based on the distribution of risk alleles for the controls.
• the proportion of log familial relative risk (FRR; the odds ratio for colorectal cancer associated with having a first-degree relative with colorectal cancer) that could be attributable to the risk alleles of the SNPs was estimated.
• FRR log familial relative risk
• the Hardy-Weinberg equilibrium for each SNP, linkage equilibrium between the SNPs, and a multiplicative model for the associations of the SNPs with colorectal cancer risk was assumed. More precisely, let SNPi, SNP 4 5 be the known colorectal cancer-associated SNPs and let clinical factori, ... , clinical factor m be unknown ones (note: these could be any heritable factors contributing to the FRR, but for simplicity we think of them as SNPs).
• G t is a random variable giving the number of risk alleles at SNP t for a random person from the population
• log FRR is the sum of independent components from the known and unknown colorectal cancer-associated SNPs.
• the proportion of the log FRR due to the known SNPs is 1 ⁇ 2(Var(Xi)+...+Var(X 45 ))/logFRR while the proportion due to the unknown SNPs is one minus this value.
• the odds ratio for colorectal cancer was 2.27 for people in the highest decile of the number of risk alleles, and 0.45 for people in the lowest decile; this is equivalent to a 5.04-fold inter-decile risk (highest vs. lowest decile).
• the odds ratio per standard deviation of risk alleles was 1.57.
• the receiver operating characteristic curve had an area under the curve of 0.63.
• the average cumulative risk of colorectal cancer to age 70 years was 3.3%.
• the cumulative risk was 5.9% (11.5% if they also had a first-degree relative with colorectal cancer, and 5.5% if they did not) compared with 1.7% for people in the lowest quintile for number of risk alleles (3.2% if they also had a first-degree relative with colorectal cancer, and 1.6% if they did not).
• the 5-year risk of colorectal cancer for the average (previously unaffected) person in Australia reaches 1% at age 63 years.
• the same 1% 5-year risk is attained approximately 7 years earlier for people in the highest quintile for number of risk alleles (and approximately 14 years earlier if they also had a family history of colorectal cancer), and approximately 10 years earlier for people in the highest decile for number of risk alleles (16 years earlier if they also had a family history; Figure 2 Panels C, D and Table 5).
• males reached the 1% risk threshold 1-2 years earlier, and females reached the threshold on average 3-4 years later than for males and females combined (Table 5).
• Table 5 Age (years) at which the 5-year risk of colorectal cancer reaches or exceeds thresholds of 1%, for various categories of family history of colorectal cancer (at least one first-degree relative) and risk alleles of 45 SNPs.
• Simulations were used to quantify the utility of a panel of 45 risk-associated SNPs to categorize people based on their risk of colorectal cancer. People at the ends of the spectrum for risk alleles were considerably more likely to develop colorectal cancer (high end) or less likely to develop colorectal cancer (low end). Because the total variation in risk associated with these SNPs across the population can explain about one quarter of the total FRR, the predictive strength of the SNP profile is increased if family history of colorectal cancer is also taken into account.
• measurement of these SNPs is a useful method for assessment of colorectal cancer risk, and can be used as a tool for determining who should be recommended for colorectal cancer screening, and at what intensity. For example, a person in the top 20% of the population for risk alleles (at least 44 alleles) reaches the average population 5-year risk 9 years earlier than the average person. Therefore, if the average person meets the risk-threshold for fecal occult blood test screening (which most national screening programs recommend) at age 50 years, then a person with at least 44 risk alleles reaches the same risk-threshold at age 41 years.
• the ages to begin colonoscopy screening for people with a first-degree relative with colorectal cancer would be 49 and 47 years for the highest quintile and the highest decile of risk alleles respectively.
• the 2% threshold for being in the top quintile or decile and having a family history of colorectal cancer is reached at ages 62 and 59 years respectively.
• a family history-based risk score that gives a log transformed age-adjusted 5- year colorectal cancer risk based on multi-generational colorectal cancer data using a mixed major gene - polygenic model (CRiPT) was determined. This clinical risk assessment was combined with the risk score based of the 45 SNPs listed in Table 4. The inventors used logistic regression to estimate the odds ratio per adjusted standard deviation (OPERA) (Dite et al., 2016) for each score with colorectal cancer risk.
• OPERA odds ratio per adjusted standard deviation
• the SNP-based score, the family history-based score, and the combined SNP and family history-based scores all associated with colorectal cancer risk with OPERAs of 1.40 (95% confidence interval [CI], 1.24-1.58), 1.39 (1.26-1.53), and 1.59 (1.42- 1.79), respectively. These are equivalent to inter-quartile risk ratios (risk in highest 25% of the population for the risk score divided by the risk in the lowest 25% of the population) of 2.4, 2.3 and 3.2.
• the combined risk score gave better fits than the SNP- and family history-based scores (both P ⁇ 0.001).

Landscapes

• Chemical & Material Sciences (AREA)
• Life Sciences & Earth Sciences (AREA)
• Health & Medical Sciences (AREA)
• Organic Chemistry (AREA)
• Proteomics, Peptides & Aminoacids (AREA)
• Engineering & Computer Science (AREA)
• Genetics & Genomics (AREA)
• Analytical Chemistry (AREA)
• Molecular Biology (AREA)
• Zoology (AREA)
• Wood Science & Technology (AREA)
• Physics & Mathematics (AREA)
• Bioinformatics & Cheminformatics (AREA)
• Immunology (AREA)
• Biochemistry (AREA)
• Pathology (AREA)
• Biophysics (AREA)
• General Health & Medical Sciences (AREA)
• Biotechnology (AREA)
• Microbiology (AREA)
• General Engineering & Computer Science (AREA)
• Oncology (AREA)
• Hospice & Palliative Care (AREA)
• Chemical Kinetics & Catalysis (AREA)
• Medicinal Chemistry (AREA)
• General Chemical & Material Sciences (AREA)
• Bioinformatics & Computational Biology (AREA)
• Evolutionary Biology (AREA)
• Medical Informatics (AREA)
• Spectroscopy & Molecular Physics (AREA)
• Theoretical Computer Science (AREA)
• Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)
• Investigating Or Analysing Biological Materials (AREA)
• Medical Treatment And Welfare Office Work (AREA)
• Apparatus Associated With Microorganisms And Enzymes (AREA)

Priority Applications (11)

Applications Claiming Priority (4)

Publications (1)

Family

ID=59396853

Family Applications (1)

Country Status (11)

Families Citing this family (8)

Citations (8)

Family Cites Families (11)

• 2017
  • 2017-01-27 EP EP17743495.8A patent/EP3408412B1/en active Active
  • 2017-01-27 AU AU2017212152A patent/AU2017212152B2/en active Active
  • 2017-01-27 JP JP2018539370A patent/JP7126704B2/ja active Active
  • 2017-01-27 IL IL260777A patent/IL260777B/en unknown
  • 2017-01-27 CA CA3012783A patent/CA3012783A1/en active Pending
  • 2017-01-27 US US16/074,032 patent/US11773448B2/en active Active
  • 2017-01-27 CN CN201780021329.8A patent/CN109072308A/zh active Pending
  • 2017-01-27 WO PCT/AU2017/050066 patent/WO2017127893A1/en active Application Filing
  • 2017-01-27 SG SG11201806432PA patent/SG11201806432PA/en unknown
  • 2017-01-27 MX MX2018009254A patent/MX2018009254A/es unknown
  • 2017-01-27 KR KR1020187024547A patent/KR20180123480A/ko active Search and Examination
• 2022
  • 2022-03-24 JP JP2022048257A patent/JP2022104934A/ja not_active Withdrawn

Patent Citations (9)

Non-Patent Citations (26)

Also Published As

Similar Documents

Legal Events